title: Using keywords to predict the need for an audiogram: an analysis of referral letters using logistic regression.
abstract: BACKGROUND
  Otolaryngology clinics are often booked without considering the distribution of work for doctors and audiologists. This causes inefficiencies of time and human resources. This may be due to clinics being booked before referrals have been triaged to identify whether a hearing test, known as a pure tone audiogram (PTA), is indicated. A model that can predict the need for PTA without clinician-led triage could be useful to address these booking issues.
  
  
  OBJECTIVE
  To establish if it is possible to predict whether a referred patient requires a PTA based on occurrences of words in the referral letter.
  
  
  METHODS
  Binary logistic regression analysis of 500 letters of referral for otolaryngology outpatients. The derived model was then tested on 50 referral letters. All the referral letters were reviewed by clinicians and classified according to whether or not a PTA would be required.
  
  
  RESULTS
  The regression model correctly predicted requirement for a PTA in 42 of 50 referral letters (84%), with a sensitivity of 91% and specificity of 82%.
  
  
  CONCLUSIONS
  The model is able to predict requirement for a PTA from referral letters with a high degree of accuracy. This method may have a role in assisting administrative/clerical staff or non-specialist clinicians to book appropriate ear, nose and throat (ENT) clinic appointments, with or without a PTA. As a result, workload would be distributed more evenly, through the clinic for both otolaryngologists and audiologists, increasing efficiency.
authors.affiliation: ENT Department, Royal Cornwall Hospital, Truro, Cornwall, UK. vmreddy9@gmail.com
authors.forename: Venkat M
authors.initials: VM
authors.lastname: Reddy
authors.forename: Tarig
authors.initials: T
authors.lastname: Abdelrahman
authors.forename: Andrew
authors.initials: A
authors.lastname: Lau
authors.forename: Kel
authors.initials: K
authors.lastname: Anyanwu
authors.forename: David Jd
authors.initials: DJ
authors.lastname: Whinney
mesh_terms: D001299:Audiometry / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012017:Referral and Consultation / Q000706:statistics & numerical data*